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Bioinformatics Analysis Reveals Meaningful Markers and Outcome Predictors in HBV‑Associated Hepatocellular Carcinoma

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Bioinformatics Analysis Reveals Meaningful Markers and Outcome Predictors in HBV‑Associated Hepatocellular Carcinoma EXPERIMENTAL AND THERAPEUTIC MEDICINE 20: 427-435, 2020 Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV‑associated hepatocellular carcinoma LIJIE ZHANG, JOYMAN MAKAMURE, DAN ZHAO, YIMING LIU, XIAOPENG GUO, CHUANSHENG ZHENG and BIN LIANG Department of Radiology, Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, P.R. China Received April 20, 2019; Accepted December 5, 2019 DOI: 10.3892/etm.2020.8722 Abstract. Hepatocellular carcinoma (HCC) is the most the high degree of connectivity by using Cytoscape software. common type of malignant neoplasm of the liver with high In addition, overall survival (OS) and disease‑free survival morbidity and mortality. Extensive research into the pathology (DFS) analyses were performed using the Gene Expression of HCC has been performed; however, the molecular Profiling Interactive Analysis online database, which revealed mechanisms underlying the development of hepatitis B that the increased expression of all hub genes were associated virus-associated HCC have remained elusive. Thus, the present with poorer OS and DFS outcomes. Receiver operating study aimed to identify critical genes and pathways associated characteristic curves were constructed using GraphPad prism with the development and progression of HCC. The expression 7.0 software. The results confirmed that 15 hub genes were profiles of the GSE121248 dataset were downloaded from the able to distinguish HCC form normal tissues. Furthermore, the Gene Expression Omnibus database and the differentially expression levels of three key genes were analyzed in tumor expressed genes (DEGs) were identified. Gene Ontology and normal samples of the Human Protein Atlas database. The (GO) and Kyoto Encyclopedia of Gene and Genome present results may provide further insight into the underlying (KEGG) analyses were performed by using the Database mechanisms of HCC and potential therapeutic targets for the for Annotation, Visualization and Integrated Discovery. treatment of this disease. Subsequently, protein‑protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, Introduction 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO Hepatocellular carcinoma (HCC) is the third leading cause of analysis revealed that the DEGs were primarily enriched in cancer-associated mortality worldwide and is highly associ- ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. ated with hepatitis C and B infection, as well as alcohol KEGG analysis indicated that DEGs were accumulated in abuse (1). In East Asia and sub‑Saharan Africa, chronic ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty hepatitis B infection is a vital factor in the pathogenesis of acid degradation’. After constructing the PPI network, liver cancer (2). To date, numerous studies have identified cyclin‑dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest hepatitis B virus (HBV)-associated factors, including hepatitis deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding B e antigen and the hepatitis B surface antigen, as key predic- uninhibited by benzimidazoles (BUB)1, TTK protein kinase, tors of HBV-associated HCC development in patients with non‑SMC condensin I complex subunit G, NDC80 kinetochore hepatitis B infection (3). In addition, the viral load of HBV, complex component, aurora kinase A, kinesin family member duration of infection and severity of liver disease have been 11, cell division cycle 20, BUB1B and abnormal spindle linked to the risk of developing cirrhosis (2,4); however, the microtubule assembly were identified as hub genes based on mechanisms of HBV-associated HCC development remain to be fully elucidated, which poses challenges for the clinical treatment of this disease. Post-operative recurrence is the most imperative issue to overcome (5). Bioinformatics analyses may be applied to determine the potential mechanisms underlying Correspondence to: Professor Bin Liang, Department of Radiology, the development of HBV-associated HCC. Furthermore, Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, predictive biomarkers may improve the prediction of patient 1277 Jiefang Road, Wuhan, Hubei 430022, P.R. China prognosis and the understanding of the molecular mechanisms E-mail: [email protected] underlying this disease (6). α-Fetoprotein, associated with gene duplications (7), was Key words: bioinformatics analysis, hepatocellular carcinoma, the sole biomarker reported in randomized controlled trials (8). biomarker, differentially expressed genes, hepatitis B virus As only a small number of biomarkers have been identified, developments in the treatment and diagnosis of HCC, as well as prognostic evaluation, are restricted. With recent advances 428 ZHANG et al: BIOINFORMATICS ANALYSIS OF HBV‑ASSOCIATED HCC in high-throughput technologies, an increasing number of gene to evaluate and construct a PPI network of target genes and chips are available for the detection of differentially expressed DEGs, including downregulated and upregulated genes, was genes (DEGs); thus, bioinformatics analyses of associated used. Subsequently, Cytoscape software (17) was utilized to data are frequently being performed (9). Based on these select hub genes in the PPI network. In addition, DAVID was novel techniques, databases of vast core gene data have been used to perform GO and KEGG analyses to obtain additional produced, including the National Center for Biotechnology information on the hub genes. Information (NCBI) Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (10). Analysis of these freely avail- Association of hub genes with clinical outcomes and diag‑ able gene expression data may lead to identification of DEGs nostic value. Overall survival (OS) and disease‑free survival in tumor vs. normal tissue, which may provide novel insight (DFS) analyses of hub genes were performed using the Gene into the development of cancers. Jiao et al (11) reported that Expression Profiling Interactive Analysis GEPIA)( web server upregulated eukaryotic translation initiation factor 2B subunit (http://gepia.cancer‑pku.cn/); log‑rank P<0.05 was considered ε (EIF2B5) expression was associated with HCC development to indicate statistical significance. Immunohistochemical based on GSE54236 and GSE76427 microarray data, indi- data for three hub genes with the highest degrees in patients cating that EIF2B5 may be employed as a novel biomarker for with or without HCC were then downloaded from the Human patients with liver cancer. Protein Atlas (HPA; https://www.proteinatlas.org/). In addi- In the present study, the gene expression dataset tion, GraphPad prism 7 software (GraphPad Software, Inc.) GSE121248, including hepatitis B‑induced HCC and adjacent was used to generate receiver operating characteristic (ROC) normal samples, was subjected to bioinformatics analysis. curves to determine the diagnostic value of the 15 hub genes The DEGs were screened and the functions and associated in HCC. pathways in HBV-associated HCC were further evaluated. The core genes identified in the present study may be considered Results as potential novel targets for the treatment of HBV-associated HCC. The present results may also improve the understanding Identification of DEGs in HBV‑associated HCC. The dataset of the development and recurrence of this disease, and provide comprised 107 samples, including 70 tumor samples and 37 a basis for developments regarding its clinical treatment. adjacent normal tissue samples. The GEO2R tool was used to identify the DEGs, with adjusted P<0.05 and |logFC|>1 selected Materials and methods as the cut‑off criteria. A total of 1,153 DEGs, comprising 376 downregulated and 777 upregulated genes, were retrieved by Data sourcing and identification of DEGs. The GSE121248 analyzing the GSE121248 dataset. All DEGs were included in microarray dataset was acquired and downloaded from the the subsequent analysis. GEO database (http://www.ncbi.nlm.nih.gov/geo/), on the basis of the GPL570 platform analyzed by the Affymetrix GO functional and KEGG pathway enrichment analyses Human Genome U133 Plus 2.0 Array, and included the data of DEGs. After the DEGs were identified, DAVID was of 70 HBV-associated HCC tumor samples and 37 adjacent employed to perform GO functional and KEGG pathway normal tissue samples. Simultaneously, the Series Matrix enrichment analyses of the DEGs. The data of the DEGs File of GSE121248 was downloaded. In order to obtain more were analyzed with the tool; in the GO analysis, the func- meaningful targets for the clinical application, |log fold change tional terms in three different categories, namely cellular (FC)|>1 (12) was set as the limit and GEO2R (https://www. component, biological process and molecular function ncbi.nlm.nih.gov/geo/geo2r/) was used. Genes were identified enriched by the DEGs were determined. In summary, the as significant DEGs based on adjusted P<0.05 (13), which was DEGs were significantly accumulated in cellular component applied to reduce the false-positive rate. terms including ‘protein binding’ (GO:0005515), ‘cytoplasm’ (GO:0005737), ‘extracellular exosome’ (GO:0070062) and Gene Ontology (GO) functional and Kyoto Encyclopedia of ‘cytosol’ (GO:005829). Specifically, upregulated genes were Genes and Genomes (KEGG)
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